Continuous Plant-Based and Remote Sensing for Determination of Fruit Tree Water Status

Horticulturae, Journal Year: 2024, Volume and Issue: 10(5), P. 516 - 516

Published: May 16, 2024

Climate change poses significant challenges to agricultural productivity, making the efficient management of water resources essential for sustainable crop production. The assessment plant status is crucial understanding physiological responses stress and optimizing practices in agriculture. Proximal remote sensing techniques have emerged as powerful tools non-destructive, efficient, spatially extensive monitoring status. This review aims examine recent advancements proximal methodologies utilized assessing status, consumption, irrigation needs fruit tree crops. Several proved useful continuous estimation but strong limitations terms spatial variability. On contrary, technologies, although less precise estimates, can easily cover from medium large areas with drone or satellite images. integration would definitely improve assessment, resulting higher accuracy by integrating temporal scales. paper consists three parts: first part covers current plant-based tools, second techniques, third includes an update on combined use two methodologies.

Language: Английский

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A wearable electrochemical sensor for the monitoring of neonicotinoid insecticides, salicylic acid and the pH in plant guttation

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 498, P. 155340 - 155340

Published: Aug. 30, 2024

Language: Английский

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High sensitivity and fast response wireless humidity sensor enabled by MXene-Lys for finger proximity detection and health monitoring applications

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 159474 - 159474

Published: Jan. 1, 2025

Language: Английский

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LeafDNet: Transforming Leaf Disease Diagnosis Through Deep Transfer Learning

Plant Direct, Journal Year: 2025, Volume and Issue: 9(2)

Published: Feb. 1, 2025

ABSTRACT The health and productivity of plants, particularly those in agricultural horticultural industries, are significantly affected by timely accurate disease detection. Traditional manual inspection methods labor‐intensive, subjective, often inaccurate, failing to meet the precision required modern practices. This research introduces an innovative deep transfer learning method utilizing advanced version Xception architecture, specifically designed for identifying plant diseases roses, mangoes, tomatoes. proposed model additional convolutional layers following base combined with multiple trainable dense layers, incorporating regularization dropout techniques optimize feature extraction classification. architectural enhancement enables capture complex, subtle patterns within leaf images, contributing more robust identification. A comprehensive dataset comprising 5491 images across four distinct categories was employed training, validation, testing model. experimental results showcased outstanding performance, achieving 98% accuracy, 99% precision, recall, a F1‐score. outperformed traditional as well other learning‐based methods. These emphasize potential this framework scalable, efficient, highly solution early detection, providing substantial benefits management supporting sustainable

Language: Английский

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A wearable and breathable sensor based on gradient porous electrodes for real-time leaf moisture monitoring

Sensors and Actuators B Chemical, Journal Year: 2025, Volume and Issue: unknown, P. 137461 - 137461

Published: Feb. 1, 2025

Language: Английский

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Impact of carbon nanodot uptake on complex impedance charge transport and energy storage mechanism in aloe vera leaves

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 3, 2025

Language: Английский

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Surface-enhanced spectroscopy technology based on metamaterials

Microsystems & Nanoengineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: April 3, 2025

Abstract Surface-enhanced spectroscopy technology based on metamaterials has flourished in recent years, and the use of artificially designed subwavelength structures can effectively regulate light waves electromagnetic fields, making it a valuable platform for sensing applications. With continuous improvement theory, several effective universal modes have gradually formed, including localized surface plasmon resonance (LSPR), Mie resonance, bound states continuum (BIC), Fano resonance. This review begins by summarizing these core mechanisms, followed comprehensive overview six main surface-enhanced techniques across spectrum: fluorescence (SEF), Raman scattering (SERS), infrared absorption (SEIRA), terahertz (THz) sensing, refractive index (RI) chiral sensing. These cover wide spectral range address various optical characteristics, enabling detection molecular fingerprints, structural chirality, changes. Additionally, this summarized combined different enhanced spectra, integration with other advanced technologies, status miniaturized metamaterial systems. Finally, we assess current challenges future directions. Looking to future, anticipate that metamaterial-based will play transformative role real-time, on-site scientific, environmental, biomedical fields.

Language: Английский

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Wearable Plant Sensing Devices for Health Monitoring

Wearable electronics., Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

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Ferroelectric and FeFET Devices as Biosensors

Published: April 11, 2025

Language: Английский

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Wearable electrochemical bioelectronics for agriculture

Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: 12(34), P. 22396 - 22416

Published: Jan. 1, 2024

Recent advances in wearable electrochemical bioelectronics offer promising solutions for sensitive, real-time detection of biomarkers agriculture.

Language: Английский

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